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Using Event Studies as an Outcome in Causal Analysis

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  • Dmitry Arkhangelsky
  • Kazuharu Yanagimoto
  • Tom Zohar

Abstract

We propose a causal framework for applications where the outcome of interest is a unit-specific response to events, which first needs to be measured from the data. We suggest a two-step procedure: first, estimate unit-level event studies (ULES) by comparing pre- and post-event outcomes of each unit to a suitable control group; second, use the ULES in causal analysis. We outline the theoretical conditions under which this two-step procedure produces interpretable results, highlighting the underlying statistical challenges. Our method overcomes the limitations of regression-based approaches prevalent in the empirical literature, allowing for a deeper examination of heterogeneity and dynamic effects. We apply this framework to analyze the impact of childcare provision reform on the magnitude of child penalties in the Netherlands, illustrating its ability to reveal nuanced positive relationships between childcare provision and parental labor supply. In contrast, traditional regression-based analysis delivers negative effects, thereby emphasizing the benefits of our two-step approach.

Suggested Citation

  • Dmitry Arkhangelsky & Kazuharu Yanagimoto & Tom Zohar, 2024. "Using Event Studies as an Outcome in Causal Analysis," Papers 2403.19563, arXiv.org, revised Jan 2025.
    • Handle: RePEc:arx:papers:2403.19563
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    1. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    2. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iván Fernández-Val, 2018. "Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," NBER Working Papers 24678, National Bureau of Economic Research, Inc.
    3. V Chernozhukov & W K Newey & R Singh, 2023. "A simple and general debiased machine learning theorem with finite-sample guarantees," Biometrika, Biometrika Trust, vol. 110(1), pages 257-264.
    4. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    5. Manuel Arellano & Stéphane Bonhomme, 2012. "Identifying Distributional Characteristics in Random Coefficients Panel Data Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(3), pages 987-1020.
    6. Martin Eckhoff Andresen & Emily Nix, 2022. "Can the child penalty be reduced?. Evaluating multiple policy interventions," Discussion Papers 983, Statistics Norway, Research Department.
    7. Sun, Liyang & Abraham, Sarah, 2021. "Estimating dynamic treatment effects in event studies with heterogeneous treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 175-199.
    8. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, July.
    9. Vira Semenova & Victor Chernozhukov, 2021. "Debiased machine learning of conditional average treatment effects and other causal functions," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 264-289.
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    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth

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